A Framework for the Systematic Review of ML Assets in AI Registries
The work proposes a framework to systematically retrieve Machine Learning assets from AI registries, mirroring scientific literature reviews. It structures this process into ten steps—from planning to documenting—to ensure asset selection is transparent and reproducible. This methodology addresses the mismatch between traditional review methods and the dynamic, heterogeneous nature of AI ecosystems.